2008_IJCV_LabelMe%3AADatabaseandWeb-BasedToolforImageAnnotation英文书.pdf

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2008_IJCV_LabelMe%3AADatabaseandWeb-BasedToolforImageAnnotation英文书.pdf

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Int J Comput Vis (2008) 77: 157–173 DOI 10.1007/s11263-007-0090-8 LabelMe: A Database and Web-Based Tool for Image Annotation Bryan C. Russell · Antonio Torralba · Kevin P. Murphy · William T. Freeman Received: 6 September 2005 / Accepted: 11 September 2007 / Published online: 31 October 2007 ? Springer Science+Business Media, LLC 2007 Abstract We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web. Keywords Database · Annotation tool · Object recognition · Object detection The ?rst two authors (B.C. Russell and A. Torralba) contributed equally to this work. B.C. Russell ( ) · A. Torralba · W.T. Freeman Computer Science and Arti?cial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA e-mail: brussell@ A. Torralba e-mail: torralba@ W.T. Freeman e-mail: billf@ K.P. Murphy Departments of computer science and statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada e-mail: murphyk@cs.ubc.ca 1 Introduction Thousands of objects occupy the visual world in which we live. Biederman (1987) estimates that humans can recognize about 30 000 entry-level object categories. Recent work in computer vision has shown impre